Statistics-balanced Loss.
Various losses have been proposed to deal with imbalance or long-tail distributions using weighted losses. The most popular loss is the inverse-frequency loss (Weighted Cross-Entropy). It weights the cross entropy loss of each class by its inverse frequency. Class-Balanced Loss [1]} motivates a weighted cross entropy loss with the concept of effective number of samples in each class. LDAM [2]} also derives a weighted cross entropy loss based on margins between classes. Some losses require changes to the network and require iterative optimization process so they are not easily adaptable to semantic segmentation. We compare to the representative Class-Balanced loss in our experiments.
